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About 10,000 papers for “Search algorithm”

Ovid search algorithm.

Phillip Staibano (15204566), Emily Oulousian (18625361), Tyler McKechnie (15181025) et al. · Figshare · 2024

<div><p>Importance</p><p>Adaptive surgical trials are scarce, but adopting these methods may help elevate the quality of surgical research when large-scale RCTs are impractical.</p><p>Objective</p><p>Randomized-controlled trials (RCTs) are the gold standard for evidence-based healthcare. Despite an increase in the number of RCTs, the number of surgical trials remains unchanged. Adaptive clinical trials can streamline trial design and time to trial reporting. The advantages identified for ACTs may help to improve the quality of future surgical trials. We present a scoping review of the methodological and reporting quality of adaptive surgical trials.</p><p>Evidence review</p><p>We performed a search of Ovid, Web of Science, and Cochrane Collaboration for all adaptive surgical RCTs performed from database inception to October 12, 2023. We included any published trials that had at least one surgical arm. All review and abstraction were performed in duplicate. Risk of bias (RoB) was assessed using the RoB 2.0 instrument and reporting quality was evaluated using CONSORT ACE 2020. All results were analyzed using descriptive methods.</p><p>Findings</p><p>Of the 1338 studies identified, six trials met inclusion criteria. Trials were performed in cardiothoracic, oral, orthopedic, and urological surgery. The most common type of adaptive trial was group sequential design with pre-specified interim analyses planned for efficacy, futility, and/or sample size re-estimation. Two trials did use statistical simulations. Our risk of bias evaluation identified a high risk of bias in 50% of included trials. Reporting quality was heterogeneous regarding trial design and outcome assessment and details in relation to randomization and blinding concealment.</p><p>Conclusion and relevance</p><p>Surgical trialists should consider implementing adaptive components to help improve patient recruitment and reduce trial duration. Reporting of future adaptive trials must adhere to existing CONSORT ACE 2020 guidelines. Future research is needed to optimize standardization of adaptive methods across medicine and surgery.</p></div>

Cited by 0Open access

Literature search algorithm.

Mo Jin-A, Lim Ju-Hee, Sul Ah-Ram et al. · Figshare · 2015

<p>Searching through the literature identified 1515 documents; a further 62 documents were identified using hand searching. Of these, 1097 documents met our exclusion criteria. 451documents duplicated data from other reports and were also excluded. A total of 17studieswere included in the final evaluation.</p>

Cited by 0Open access

Harmony Search Algorithm

Alireza Askarzadeh, Esmat Rashedi · Intelligent Systems · 2018

Harmony search (HS) is a meta-heuristic search algorithm which tries to mimic the improvisation process of musicians in finding a pleasing harmony. In recent years, due to some advantages, HS has received a significant attention. HS is easy to implement, converges quickly to the optimal solution and finds a good enough solution in a reasonable amount of computational time. The merits of HS algorithm have led to its application to optimization problems of different engineering areas. In this chapter, the concepts and performance of HS algorithm are shown and some engineering applications are reviewed. It is observed that HS has shown promising performance in solving difficult optimization problems and different versions of this algorithm have been developed. In the next years, it is expected that HS is applied to more real optimization problems.

Cited by 12

Predetermined search algorithm.

Hannah N. Marmor (14046394), Mindy Pike (10894007), Zhiguo (Alex) Zhao (14046397) et al. · Figshare · 2022

Cited by 0Open access

Cuckoo search algorithm with deep search

Cai Zefan, Yang Xiaodong · 2017

Cuckoo search algorithm (CS) is a kind of bionic swarm optimization algorithm, which is simple and convenient. Although it has obvious advantages, it cannot converge to the optimal solution when dealing with high dimensional complex problems, so its global search ability needs to be improved. In this paper, opposition-based learning (OBL) strategy and local enhanced search are introduced to improve the basic CS. In the selection stage, an opposition-based swarm (ob-swarm) is generated on the basis of the original swarm. If an old cuckoo individual from the original swarm may be discarded, three cuckoo individuals are randomly selected from the ob-swarm to produce a new individual. The old one will be replaced by the new one if the new one is better. At the end of each generation, a potential optimal solution will be searched for locally around the current global optimal solution in the evolution direction. This local searching operation can make up the problem that the search step may be not appropriate. The simulation results show that the improved algorithm improves the global search ability, convergence speed and convergence precision of the algorithm.

Cited by 21